RNASeq Comparison between Ubiquitous and ISC Datasets
Ellora Chua
Heatmaps
Ubiquitous
experimental_metadata <- read.delim("data/ubiquitous/metadata_rem.txt", sep=",", header=TRUE, stringsAsFactors=FALSE)
annotation = data.frame(Condition = rep(c("Control","OKSM", "Senolytic", "Senolyic_OKSM"),
c(3, 3, 2, 3)))
row.names(annotation) = experimental_metadata$sample_id
anno_colours = list(Condition = c(Control = "#BF3100", OKSM = "#E9B44C", Senolytic = "#1B998B", Senolyic_OKSM = "#5D576B"))pheatmap(ubiq_pam_clust[,1:(ncol(ubiq_pam_clust)-1)],
color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(100),
fontsize_row = 5.5,
annotation_col = annotation,
annotation_colors = anno_colours,
cluster_rows = FALSE,
cluster_cols = FALSE)
pam_clust_df %>%
dplyr::select(gene_name, Cluster) %>%
datatable(options = list(scrollX = TRUE), class = "white-space: nowrap")ubi_c1 <- pam_clust_df[pam_clust_df$Cluster == 1, ] %>%
dplyr::select(-Cluster)
ubi_c2 <- pam_clust_df[pam_clust_df$Cluster == 2, ] %>%
dplyr::select(-Cluster)
ubi_c3 <- pam_clust_df[pam_clust_df$Cluster == 3, ] %>%
dplyr::select(-Cluster)
ubi_c4 <- pam_clust_df[pam_clust_df$Cluster == 4, ] %>%
dplyr::select(-Cluster)
ubi_c5 <- pam_clust_df[pam_clust_df$Cluster == 5, ] %>%
dplyr::select(-Cluster)
ubi_c6 <- pam_clust_df[pam_clust_df$Cluster == 6, ] %>%
dplyr::select(-Cluster)
ubi_c7 <- pam_clust_df[pam_clust_df$Cluster == 7, ] %>%
dplyr::select(-Cluster)
data.frame(Cluster = c("Cluster 1", "Cluster 2", "Cluster 3", "Cluster 4", "Cluster 5",
"Cluster 6", "Cluster 7", "Total"),
Number_of_genes = c(nrow(ubi_c1), nrow(ubi_c2), nrow(ubi_c3), nrow(ubi_c4),nrow(ubi_c5),
nrow(ubi_c6), nrow(ubi_c7),
sum(c(nrow(ubi_c1),nrow(ubi_c2),nrow(ubi_c3),nrow(ubi_c4),nrow(ubi_c5),
nrow(ubi_c6),nrow(ubi_c7)))))## Cluster Number_of_genes
## 1 Cluster 1 201
## 2 Cluster 2 157
## 3 Cluster 3 173
## 4 Cluster 4 100
## 5 Cluster 5 147
## 6 Cluster 6 204
## 7 Cluster 7 300
## 8 Total 1282ISC
pheatmap(isc_pam_clust[,1:(ncol(isc_pam_clust)-1)],
color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(100),
fontsize_row = 5.5,
annotation_col = annotation,
annotation_colors = anno_colours,
cluster_rows = FALSE,
cluster_cols = FALSE)
isc_pam_clust_df <- isc_pam_clust %>%
as.data.frame() %>%
mutate(gene_name = ensembl.genes[rownames(.),]$external_gene_name)
datatable(isc_pam_clust_df, options = list(scrollX = TRUE), class = "white-space: nowrap")isc_c1 <- isc_pam_clust_df[isc_pam_clust_df$Cluster == 1, ] %>%
dplyr::select(-Cluster)
isc_c2 <- isc_pam_clust_df[isc_pam_clust_df$Cluster == 2, ] %>%
dplyr::select(-Cluster)
isc_c3 <- isc_pam_clust_df[isc_pam_clust_df$Cluster == 3, ] %>%
dplyr::select(-Cluster)
isc_c4 <- isc_pam_clust_df[isc_pam_clust_df$Cluster == 4, ] %>%
dplyr::select(-Cluster)
isc_c5 <- isc_pam_clust_df[isc_pam_clust_df$Cluster == 5, ] %>%
dplyr::select(-Cluster)
isc_c6 <- isc_pam_clust_df[isc_pam_clust_df$Cluster == 6, ] %>%
dplyr::select(-Cluster)
isc_c7 <- isc_pam_clust_df[isc_pam_clust_df$Cluster == 7, ] %>%
dplyr::select(-Cluster)
data.frame(Cluster = c("Cluster 1", "Cluster 2", "Cluster 3", "Cluster 4",
"Cluster 5", "Cluster 6", "Cluster 7", "Total"),
Number_of_genes = c(nrow(isc_c1), nrow(isc_c2), nrow(isc_c3), nrow(isc_c4),
nrow(isc_c5), nrow(isc_c6), nrow(isc_c7),
sum(c(nrow(isc_c1),nrow(isc_c2),nrow(isc_c3),nrow(isc_c4),
nrow(isc_c5),nrow(isc_c6),nrow(isc_c7)))))## Cluster Number_of_genes
## 1 Cluster 1 773
## 2 Cluster 2 397
## 3 Cluster 3 128
## 4 Cluster 4 566
## 5 Cluster 5 613
## 6 Cluster 6 248
## 7 Cluster 7 242
## 8 Total 2967Gene Expression Boxplots
Ubiquitous
Cluster 1
cluster_boxplot(ubi_c1, "Cluster 1", "TdTom", "Sen_OKSM", "OKSM")
Cluster 2
cluster_boxplot(ubi_c2, "Cluster 2", "TdTom", "Sen_OKSM", "OKSM")
Cluster 3
cluster_boxplot(ubi_c3, "Cluster 3", "TdTom", "Sen_OKSM", "OKSM")
Cluster 4
cluster_boxplot(ubi_c4, "Cluster 4", "TdTom", "Sen_OKSM", "OKSM")
Cluster 5
cluster_boxplot(ubi_c5, "Cluster 5", "TdTom", "Sen_OKSM", "OKSM")
Cluster 6
cluster_boxplot(ubi_c6, "Cluster 6", "TdTom", "Sen_OKSM", "OKSM")
Cluster 7
cluster_boxplot(ubi_c7, "Cluster 7", "TdTom", "Sen_OKSM", "OKSM")
isc
Cluster 1
cluster_boxplot(isc_c1, "Cluster 1", "Control", "SO", "O")
Cluster 2
cluster_boxplot(isc_c2, "Cluster 2", "Control", "SO", "O")
Cluster 3
cluster_boxplot(isc_c3, "Cluster 3", "Control", "SO", "O")
Cluster 4
cluster_boxplot(isc_c4, "Cluster 4", "Control", "SO", "O")
Cluster 5
cluster_boxplot(isc_c5, "Cluster 5", "Control", "SO", "O")
Cluster 6
cluster_boxplot(isc_c6, "Cluster 6", "Control", "SO", "O")
Cluster 7
cluster_boxplot(isc_c7, "Cluster 7", "Control", "SO", "O")
Gene overlaps
Overall
olaps <- readPNG("output/comparison/overlaps.png")
p <- rasterGrob(olaps, interpolate = TRUE)
ggarrange(p)
What are these 686 genes that overlap?
overlapping_genes <- pam_clust_df[rownames(pam_clust_df) %in% rownames(isc_pam_clust_df),]
overlapping_genes %>%
dplyr::select(gene_name, Cluster) %>%
rename("Gene Name" = gene_name) %>%
datatable(options = list(scrollX = TRUE))Where are they located in the ubiquitous dataset?
overlapping_genes %>%
dplyr::select(gene_name, Cluster) %>%
group_by(Cluster) %>%
tally() %>%
datatable(options = list(scrollX = TRUE), caption = "Where the 686 genes are located in the ubiquitous dataset")What are these overlapping genes enriched for?
GO
eresList$GO_Biological_Process_2018 %>%
plot_enrichr("GO_Biological_Process_2018")
datatable(eresList[[1]], options = list(scrollX = TRUE), class = "white-space: nowrap")KEGG
eresList$KEGG_2019 %>%
plot_enrichr("KEGG_2019") 
datatable(eresList[[2]], options = list(scrollX = TRUE), class = "white-space: nowrap")Where in the ISC dataset are these 686 genes?
isc_pam_clust_df[rownames(isc_pam_clust_df) %in% rownames(pam_clust_df),] %>%
dplyr::select(gene_name, Cluster) %>%
group_by(Cluster) %>%
tally() %>%
datatable(options = list(scrollX = TRUE), caption = "Where the 686 genes are located in the ISC dataset")I’ve manipulated the heatmaps such that Clusters 1 - 5 in the ubiquitous data is the same as Clusters 1 - 7 in the ISC dataset for easy comparison
C1 ubiquitous dataset in C1 and/or C2 of ISC dataset
Percentage of genes in c1 of ubiquitous dataset in c1 & c2 of ISC dataset
denom <- c(rownames(isc_c1),rownames(isc_c2))
sum(rownames(ubi_c1) %in% denom)/length(denom)*100## [1] 5.897436Where else can we find c1 ubiquitous genes in ISC dataset?
tab <- isc_pam_clust_df[rownames(ubi_c1),] %>%
drop_na %>%
group_by(Cluster) %>%
tally()
tab## # A tibble: 7 × 2
## Cluster n
## <dbl> <int>
## 1 1 39
## 2 2 30
## 3 3 2
## 4 4 3
## 5 5 6
## 6 6 12
## 7 7 496 genes in Cluster 1 of the ubiquitous dataset is accounted for in the ISC dataset. This implies that 1074 genes in C1 of the ubiquitous dataset is not expressed or significantly differentially expressed in the ISC dataset.
C3 ubiquitous dataset in C4 and or C5 of ISC dataset
Percentage of genes in c3 of ubiquitous dataset in c4 & c5 ISC dataset
denom <- c(rownames(isc_c4),rownames(isc_c5))
sum(rownames(ubi_c3) %in% denom)/length(denom)*100## [1] 4.240882Where else can we find c3 ubiquitous genes in ISC dataset?
tab <- isc_pam_clust_df[rownames(ubi_c3),] %>%
drop_na %>%
group_by(Cluster) %>%
tally()
tab## # A tibble: 7 × 2
## Cluster n
## <dbl> <int>
## 1 1 8
## 2 2 4
## 3 3 2
## 4 4 31
## 5 5 19
## 6 6 9
## 7 7 1487 genes in Cluster 3 of the ubiquitous dataset is accounted for in the ISC dataset. This implies that 1092 genes in C3 of the ubiquitous dataset is not expressed or significantly differentially expressed in the ISC dataset.
C4 ubiquitous dataset in C6 of ISC dataset
Percentage of genes in c4 of ubiquitous dataset in c6 ISC dataset
sum(rownames(ubi_c4) %in% rownames(isc_c6))/length(rownames(isc_c6))*100## [1] 7.258065Where else can we find c4 ubiquitous genes in ISC dataset?
tab <- isc_pam_clust_df[rownames(ubi_c4),] %>%
drop_na %>%
group_by(Cluster) %>%
tally()
tab## # A tibble: 6 × 2
## Cluster n
## <dbl> <int>
## 1 1 19
## 2 2 7
## 3 3 1
## 4 4 3
## 5 5 10
## 6 6 1858 genes in Cluster 4 of the ubiquitous dataset is accounted for in the ISC dataset. This implies that 190 genes in C4 of the ubiquitous dataset is not expressed or significantly differentially expressed in the ISC dataset.
Summary Enrichments from custom script
Ubiquitous
filenames = list.files("output/ubiquitous", pattern = "_go.rds", full.names = TRUE)
all_go = lapply(filenames, function(x){readRDS(x)})
names(all_go) <- c("c1", "c2", "c3", "c4", "c5", "c6", "c7")
ubi_go <- all_go %>%
bind_rows(.id = "Cluster") %>%
group_by(Cluster) %>%
slice_min(order_by = Adjusted.P.value, n = 10)ubi_go %>%
mutate(Term = gsub("\\([^()]*\\)", "", Term),
Term = str_to_title(Term)) %>%
mutate(Cluster = factor(Cluster, levels = c(unique(ubi_go$Cluster)))) %>%
group_by(Cluster) %>%
mutate(Term = factor(Term, levels = Term)) %>%
ggplot(aes(x = Cluster, y = Term)) +
geom_point(aes(colour = Adjusted.P.value, size = Ratio)) +
ylab(NULL) +
xlab("Cluster") +
scale_color_continuous(low="red", high="blue", guide=guide_colorbar(reverse=FALSE)) +
scale_y_discrete(labels = function(x) str_wrap(x, width = 60),
limits=rev) +
#labs(fill = "Adjusted p-value") +
guides(colour = guide_colorbar(title = "Adjusted p-value", reverse = TRUE),
size = guide_legend(title = "Gene Ratio")) +
theme(axis.text.x = element_text(size=8)) +
theme_bw() +
ggtitle("GO Enrichment Analysis")
filenames = list.files("output/ubiquitous", pattern = "_kegg.rds", full.names = TRUE)
all_kegg = lapply(filenames, function(x){readRDS(x)})
names(all_kegg) <- c("c1", "c2", "c4", "c5", "c6", "c7")
ubi_kegg <- all_kegg %>%
bind_rows(.id = "Cluster") %>%
group_by(Cluster) %>%
slice_min(order_by = Adjusted.P.value, n = 10)ubi_kegg %>%
mutate(Term = gsub("\\([^()]*\\)", "", Term),
Term = str_to_title(Term)) %>%
mutate(Cluster = factor(Cluster, levels = c(unique(ubi_kegg$Cluster)))) %>%
group_by(Cluster) %>%
mutate(Term = factor(Term, levels = Term)) %>%
ggplot(aes(x = Cluster, y = Term)) +
geom_point(aes(colour = Adjusted.P.value, size = Ratio)) +
ylab(NULL) +
xlab("Cluster") +
scale_color_continuous(low="red", high="blue", guide=guide_colorbar(reverse=FALSE)) +
scale_y_discrete(labels = function(x) str_wrap(x, width = 60),
limits=rev) +
#labs(fill = "Adjusted p-value") +
guides(colour = guide_colorbar(title = "Adjusted p-value", reverse = TRUE),
size = guide_legend(title = "Gene Ratio")) +
theme(axis.text.x = element_text(size=8)) +
theme_bw() +
ggtitle("KEGG Enrichment Analysis")
ISC
filenames = list.files("output/isc", pattern = "_go.rds", full.names = TRUE)
all_go = lapply(filenames, function(x){readRDS(x)})
names(all_go) <- c("c1", "c2", "c3", "c4", "c5", "c6", "c7")
isc_go <- all_go %>%
bind_rows(.id = "Cluster") %>%
group_by(Cluster) %>%
slice_min(order_by = Adjusted.P.value, n = 10)isc_go %>%
mutate(Term = gsub("\\([^()]*\\)", "", Term),
Term = str_to_title(Term)) %>%
mutate(Cluster = factor(Cluster, levels = c(unique(isc_go$Cluster)))) %>%
group_by(Cluster) %>%
mutate(Term = factor(Term, levels = Term)) %>%
ggplot(aes(x = Cluster, y = Term)) +
geom_point(aes(colour = Adjusted.P.value, size = Ratio)) +
ylab(NULL) +
xlab("Cluster") +
scale_color_continuous(low="red", high="blue", guide=guide_colorbar(reverse=FALSE)) +
scale_y_discrete(labels = function(x) str_wrap(x, width = 60),
limits=rev) +
#labs(fill = "Adjusted p-value") +
guides(colour = guide_colorbar(title = "Adjusted p-value", reverse = TRUE),
size = guide_legend(title = "Gene Ratio")) +
theme(axis.text.x = element_text(size=8)) +
theme_bw() +
ggtitle("GO Enrichment Analysis")
filenames = list.files("output/isc", pattern = "_kegg.rds", full.names = TRUE)
all_kegg = lapply(filenames, function(x){readRDS(x)})
names(all_kegg) <- c("c1", "c2", "c3", "c4", "c5", "c6", "c7")
isc_kegg <- all_kegg %>%
bind_rows(.id = "Cluster") %>%
group_by(Cluster) %>%
slice_min(order_by = Adjusted.P.value, n = 10)isc_kegg %>%
mutate(Term = gsub("\\([^()]*\\)", "", Term),
Term = str_to_title(Term)) %>%
mutate(Cluster = factor(Cluster, levels = c(unique(isc_kegg$Cluster)))) %>%
group_by(Cluster) %>%
mutate(Term = factor(Term, levels = Term)) %>%
ggplot(aes(x = Cluster, y = Term)) +
geom_point(aes(colour = Adjusted.P.value, size = Ratio)) +
ylab(NULL) +
xlab("Cluster") +
scale_color_continuous(low="red", high="blue", guide=guide_colorbar(reverse=FALSE)) +
scale_y_discrete(labels = function(x) str_wrap(x, width = 60),
limits=rev) +
#labs(fill = "Adjusted p-value") +
guides(colour = guide_colorbar(title = "Adjusted p-value", reverse = TRUE),
size = guide_legend(title = "Gene Ratio")) +
theme(axis.text.x = element_text(size=8)) +
theme_bw() +
ggtitle("KEGG Enrichment Analysis")
Summary Enrichments from compareCluster
Ubiquitous
genes = lapply(list(c1=ubi_c1, c2=ubi_c2, c3=ubi_c3, c4=ubi_c4, c5=ubi_c5, c6=ubi_c6, c7=ubi_c7), function(i){ensembl.genes[rownames(i)]$entrezgene_id})
bg = ensembl.genes[rownames(ddssva)]$entrezgene_idcompPathway <- compareCluster(geneCluster = genes,
fun = "enrichGO",
keyType = "ENTREZID",
ont = "BP",
OrgDb = "org.Dm.eg.db",
pvalueCutoff = 1,
qvalueCutoff = 0.1,
pAdjustMethod = "BH")
dotplot(compPathway, showCategory = 10, title = "GO Enrichment Analysis") + theme(axis.text.y = element_text(size=8))
compPathway <- compareCluster(geneCluster = genes,
fun = "enrichKEGG",
keyType = "ncbi-geneid",
organism = "dme",
pvalueCutoff = 1,
qvalueCutoff = 0.2,
pAdjustMethod = "BH")
dotplot(compPathway, showCategory = 10, title = "KEGG Enrichment Analysis") + theme(axis.text.y = element_text(size=8))
ISC
genes = lapply(list(c1=isc_c1, c2=isc_c2, c3=isc_c3, c4=isc_c4, c5=isc_c5, c6=isc_c6, c7=isc_c7), function(i){ensembl.genes[rownames(i)]$entrezgene_id})compPathway <- compareCluster(geneCluster = genes,
fun = "enrichGO",
keyType = "ENTREZID",
ont = "BP",
OrgDb = "org.Dm.eg.db",
pvalueCutoff = 1,
qvalueCutoff = 0.1,
pAdjustMethod = "BH")
dotplot(compPathway, showCategory = 8, title = "GO Enrichment Analysis") + theme(axis.text.y = element_text(size=8))
compPathway <- compareCluster(geneCluster = genes,
fun = "enrichKEGG",
keyType = "ncbi-geneid",
organism = "dme",
pvalueCutoff = 1,
qvalueCutoff = 0.1,
pAdjustMethod = "BH")
dotplot(compPathway, showCategory = 10, title = "KEGG Enrichment Analysis") + theme(axis.text.y = element_text(size=8))
Session Info
sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur 10.16
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] grid stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] ReactomePA_1.38.0 clusterProfiler_4.2.0
## [3] png_0.1-7 ggpubr_0.4.0
## [5] enrichR_3.0 DT_0.19
## [7] VennDiagram_1.7.0 futile.logger_1.4.3
## [9] RColorBrewer_1.1-2 org.Dm.eg.db_3.14.0
## [11] scales_1.1.1 reshape2_1.4.4
## [13] knitr_1.36 biomaRt_2.50.0
## [15] GenomicFeatures_1.46.1 AnnotationDbi_1.56.2
## [17] genefilter_1.76.0 DESeq2_1.34.0
## [19] SummarizedExperiment_1.24.0 Biobase_2.54.0
## [21] MatrixGenerics_1.6.0 matrixStats_0.61.0
## [23] BSgenome.Dmelanogaster.UCSC.dm6_1.4.1 BSgenome_1.62.0
## [25] rtracklayer_1.54.0 GenomicRanges_1.46.0
## [27] Biostrings_2.62.0 GenomeInfoDb_1.30.0
## [29] XVector_0.34.0 IRanges_2.28.0
## [31] S4Vectors_0.32.2 BiocGenerics_0.40.0
## [33] forcats_0.5.1 stringr_1.4.0
## [35] dplyr_1.0.7 purrr_0.3.4
## [37] readr_2.0.2 tidyr_1.1.4
## [39] tibble_3.1.6 tidyverse_1.3.1
## [41] EnhancedVolcano_1.12.0 ggrepel_0.9.1
## [43] ggplot2_3.3.5 pheatmap_1.0.12
## [45] cluster_2.1.2
##
## loaded via a namespace (and not attached):
## [1] utf8_1.2.2 tidyselect_1.1.1 RSQLite_2.2.8
## [4] htmlwidgets_1.5.4 BiocParallel_1.28.0 scatterpie_0.1.7
## [7] munsell_0.5.0 withr_2.4.2 colorspace_2.0-2
## [10] GOSemSim_2.20.0 filelock_1.0.2 highr_0.9
## [13] ggalt_0.4.0 rstudioapi_0.13 ggsignif_0.6.3
## [16] DOSE_3.20.0 Rttf2pt1_1.3.9 labeling_0.4.2
## [19] GenomeInfoDbData_1.2.7 polyclip_1.10-0 farver_2.1.0
## [22] bit64_4.0.5 rprojroot_2.0.2 downloader_0.4
## [25] treeio_1.18.0 vctrs_0.3.8 generics_0.1.1
## [28] lambda.r_1.2.4 xfun_0.29 BiocFileCache_2.2.0
## [31] R6_2.5.1 graphlayouts_0.7.1 ggbeeswarm_0.6.0
## [34] locfit_1.5-9.4 gridGraphics_0.5-1 bitops_1.0-7
## [37] cachem_1.0.6 fgsea_1.20.0 DelayedArray_0.20.0
## [40] assertthat_0.2.1 BiocIO_1.4.0 ggraph_2.0.5
## [43] enrichplot_1.14.1 beeswarm_0.4.0 gtable_0.3.0
## [46] ash_1.0-15 tidygraph_1.2.0 rlang_0.4.12
## [49] splines_4.1.2 lazyeval_0.2.2 rstatix_0.7.0
## [52] extrafontdb_1.0 checkmate_2.0.0 broom_0.7.10
## [55] yaml_2.2.1 abind_1.4-5 modelr_0.1.8
## [58] crosstalk_1.2.0 backports_1.3.0 qvalue_2.26.0
## [61] extrafont_0.17 tools_4.1.2 ggplotify_0.1.0
## [64] ellipsis_0.3.2 jquerylib_0.1.4 Rcpp_1.0.7
## [67] plyr_1.8.6 progress_1.2.2 zlibbioc_1.40.0
## [70] RCurl_1.98-1.5 prettyunits_1.1.1 viridis_0.6.2
## [73] cowplot_1.1.1 haven_2.4.3 fs_1.5.0
## [76] magrittr_2.0.1 data.table_1.14.2 futile.options_1.0.1
## [79] DO.db_2.9 reactome.db_1.77.0 reprex_2.0.1
## [82] patchwork_1.1.1 hms_1.1.1 evaluate_0.14
## [85] xtable_1.8-4 XML_3.99-0.8 readxl_1.3.1
## [88] gridExtra_2.3 compiler_4.1.2 maps_3.4.0
## [91] shadowtext_0.0.9 KernSmooth_2.23-20 crayon_1.4.2
## [94] htmltools_0.5.2 ggfun_0.0.4 tzdb_0.2.0
## [97] aplot_0.1.1 geneplotter_1.72.0 lubridate_1.8.0
## [100] DBI_1.1.1 tweenr_1.0.2 formatR_1.11
## [103] dbplyr_2.1.1 proj4_1.0-10.1 MASS_7.3-54
## [106] rappdirs_0.3.3 Matrix_1.3-4 car_3.0-12
## [109] cli_3.1.0 igraph_1.2.8 parallel_4.1.2
## [112] pkgconfig_2.0.3 GenomicAlignments_1.30.0 xml2_1.3.2
## [115] ggtree_3.2.0 annotate_1.72.0 vipor_0.4.5
## [118] bslib_0.3.1 rvest_1.0.2 yulab.utils_0.0.4
## [121] digest_0.6.28 graph_1.72.0 rmarkdown_2.11
## [124] cellranger_1.1.0 fastmatch_1.1-3 tidytree_0.3.5
## [127] restfulr_0.0.13 curl_4.3.2 graphite_1.40.0
## [130] Rsamtools_2.10.0 rjson_0.2.20 nlme_3.1-153
## [133] lifecycle_1.0.1 jsonlite_1.7.2 carData_3.0-4
## [136] viridisLite_0.4.0 fansi_0.5.0 pillar_1.6.4
## [139] lattice_0.20-45 ggrastr_1.0.1 KEGGREST_1.34.0
## [142] fastmap_1.1.0 httr_1.4.2 survival_3.2-13
## [145] GO.db_3.14.0 glue_1.5.0 bit_4.0.4
## [148] ggforce_0.3.3 stringi_1.7.5 sass_0.4.0
## [151] blob_1.2.2 memoise_2.0.0 ape_5.5